{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 准备工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "#用于计算feature字段的文本特征提取\n",
    "from sklearn.feature_extraction.text import  CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import model_selection, preprocessing, ensemble\n",
    "\n",
    "#CountVectorizer为稀疏特征，特征编码结果存为稀疏矩阵xgboost处理更高效\n",
    "from scipy import sparse\n",
    "\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "import xgboost as xgb\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold,train_test_split\n",
    "\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "#对地理位置通过聚类进行离散化\n",
    "from sklearn.cluster import KMeans\n",
    "from nltk.metrics import distance as distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>building_id</th>\n",
       "      <th>created</th>\n",
       "      <th>description</th>\n",
       "      <th>display_address</th>\n",
       "      <th>features</th>\n",
       "      <th>interest_level</th>\n",
       "      <th>latitude</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>manager_id</th>\n",
       "      <th>photos</th>\n",
       "      <th>price</th>\n",
       "      <th>street_address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "      <td>53a5b119ba8f7b61d4e010512e0dfc85</td>\n",
       "      <td>2016-06-24 07:54:24</td>\n",
       "      <td>A Brand New 3 Bedroom 1.5 bath ApartmentEnjoy ...</td>\n",
       "      <td>Metropolitan Avenue</td>\n",
       "      <td>[]</td>\n",
       "      <td>medium</td>\n",
       "      <td>40.7145</td>\n",
       "      <td>7211212</td>\n",
       "      <td>-73.9425</td>\n",
       "      <td>5ba989232d0489da1b5f2c45f6688adc</td>\n",
       "      <td>[https://photos.renthop.com/2/7211212_1ed4542e...</td>\n",
       "      <td>3000</td>\n",
       "      <td>792 Metropolitan Avenue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>c5c8a357cba207596b04d1afd1e4f130</td>\n",
       "      <td>2016-06-12 12:19:27</td>\n",
       "      <td></td>\n",
       "      <td>Columbus Avenue</td>\n",
       "      <td>[Doorman, Elevator, Fitness Center, Cats Allow...</td>\n",
       "      <td>low</td>\n",
       "      <td>40.7947</td>\n",
       "      <td>7150865</td>\n",
       "      <td>-73.9667</td>\n",
       "      <td>7533621a882f71e25173b27e3139d83d</td>\n",
       "      <td>[https://photos.renthop.com/2/7150865_be3306c5...</td>\n",
       "      <td>5465</td>\n",
       "      <td>808 Columbus Avenue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>c3ba40552e2120b0acfc3cb5730bb2aa</td>\n",
       "      <td>2016-04-17 03:26:41</td>\n",
       "      <td>Top Top West Village location, beautiful Pre-w...</td>\n",
       "      <td>W 13 Street</td>\n",
       "      <td>[Laundry In Building, Dishwasher, Hardwood Flo...</td>\n",
       "      <td>high</td>\n",
       "      <td>40.7388</td>\n",
       "      <td>6887163</td>\n",
       "      <td>-74.0018</td>\n",
       "      <td>d9039c43983f6e564b1482b273bd7b01</td>\n",
       "      <td>[https://photos.renthop.com/2/6887163_de85c427...</td>\n",
       "      <td>2850</td>\n",
       "      <td>241 W 13 Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100007</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>28d9ad350afeaab8027513a3e52ac8d5</td>\n",
       "      <td>2016-04-18 02:22:02</td>\n",
       "      <td>Building Amenities - Garage - Garden - fitness...</td>\n",
       "      <td>East 49th Street</td>\n",
       "      <td>[Hardwood Floors, No Fee]</td>\n",
       "      <td>low</td>\n",
       "      <td>40.7539</td>\n",
       "      <td>6888711</td>\n",
       "      <td>-73.9677</td>\n",
       "      <td>1067e078446a7897d2da493d2f741316</td>\n",
       "      <td>[https://photos.renthop.com/2/6888711_6e660cee...</td>\n",
       "      <td>3275</td>\n",
       "      <td>333 East 49th Street</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100013</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-28 01:32:41</td>\n",
       "      <td>Beautifully renovated 3 bedroom flex 4 bedroom...</td>\n",
       "      <td>West 143rd Street</td>\n",
       "      <td>[Pre-War]</td>\n",
       "      <td>low</td>\n",
       "      <td>40.8241</td>\n",
       "      <td>6934781</td>\n",
       "      <td>-73.9493</td>\n",
       "      <td>98e13ad4b495b9613cef886d79a6291f</td>\n",
       "      <td>[https://photos.renthop.com/2/6934781_1fa4b41a...</td>\n",
       "      <td>3350</td>\n",
       "      <td>500 West 143rd Street</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        bathrooms  bedrooms                       building_id  \\\n",
       "10            1.5         3  53a5b119ba8f7b61d4e010512e0dfc85   \n",
       "10000         1.0         2  c5c8a357cba207596b04d1afd1e4f130   \n",
       "100004        1.0         1  c3ba40552e2120b0acfc3cb5730bb2aa   \n",
       "100007        1.0         1  28d9ad350afeaab8027513a3e52ac8d5   \n",
       "100013        1.0         4                                 0   \n",
       "\n",
       "                    created  \\\n",
       "10      2016-06-24 07:54:24   \n",
       "10000   2016-06-12 12:19:27   \n",
       "100004  2016-04-17 03:26:41   \n",
       "100007  2016-04-18 02:22:02   \n",
       "100013  2016-04-28 01:32:41   \n",
       "\n",
       "                                              description  \\\n",
       "10      A Brand New 3 Bedroom 1.5 bath ApartmentEnjoy ...   \n",
       "10000                                                       \n",
       "100004  Top Top West Village location, beautiful Pre-w...   \n",
       "100007  Building Amenities - Garage - Garden - fitness...   \n",
       "100013  Beautifully renovated 3 bedroom flex 4 bedroom...   \n",
       "\n",
       "            display_address  \\\n",
       "10      Metropolitan Avenue   \n",
       "10000       Columbus Avenue   \n",
       "100004          W 13 Street   \n",
       "100007     East 49th Street   \n",
       "100013    West 143rd Street   \n",
       "\n",
       "                                                 features interest_level  \\\n",
       "10                                                     []         medium   \n",
       "10000   [Doorman, Elevator, Fitness Center, Cats Allow...            low   \n",
       "100004  [Laundry In Building, Dishwasher, Hardwood Flo...           high   \n",
       "100007                          [Hardwood Floors, No Fee]            low   \n",
       "100013                                          [Pre-War]            low   \n",
       "\n",
       "        latitude  listing_id  longitude                        manager_id  \\\n",
       "10       40.7145     7211212   -73.9425  5ba989232d0489da1b5f2c45f6688adc   \n",
       "10000    40.7947     7150865   -73.9667  7533621a882f71e25173b27e3139d83d   \n",
       "100004   40.7388     6887163   -74.0018  d9039c43983f6e564b1482b273bd7b01   \n",
       "100007   40.7539     6888711   -73.9677  1067e078446a7897d2da493d2f741316   \n",
       "100013   40.8241     6934781   -73.9493  98e13ad4b495b9613cef886d79a6291f   \n",
       "\n",
       "                                                   photos  price  \\\n",
       "10      [https://photos.renthop.com/2/7211212_1ed4542e...   3000   \n",
       "10000   [https://photos.renthop.com/2/7150865_be3306c5...   5465   \n",
       "100004  [https://photos.renthop.com/2/6887163_de85c427...   2850   \n",
       "100007  [https://photos.renthop.com/2/6888711_6e660cee...   3275   \n",
       "100013  [https://photos.renthop.com/2/6934781_1fa4b41a...   3350   \n",
       "\n",
       "                 street_address  \n",
       "10      792 Metropolitan Avenue  \n",
       "10000       808 Columbus Avenue  \n",
       "100004          241 W 13 Street  \n",
       "100007     333 East 49th Street  \n",
       "100013    500 West 143rd Street  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_json(\"/Users/admin/Desktop/day02/week2data_RentListingInquries/RentListingInquries_train.json\")\n",
    "test = pd.read_json(\"/Users/admin/Desktop/day02/week2data_RentListingInquries/RentListingInquries_test.json\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## price, bathrooms, bedrooms\n",
    "数值型特征，+／-／*／ ／\n",
    "特征的单调变换对XGBoost不必要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "def remove_noise(df):\n",
    "#remove some noise\n",
    "    df= df[df.price < 10000]\n",
    "    df.loc[df[\"bathrooms\"] == 112, \"bathrooms\"] = 1.5\n",
    "    df.loc[df[\"bathrooms\"] == 10, \"bathrooms\"] = 1\n",
    "    df.loc[df[\"bathrooms\"] == 20, \"bathrooms\"] = 2\n",
    "    \n",
    "remove_noise(train)\n",
    "remove_noise(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#构造新特征\n",
    "#price_bathrooms：单位bathroom的价格\n",
    "#price_bedrooms：单位bedroom的价格\n",
    "def create_price_room(df):\n",
    "    df['price_bathrooms'] =  (df[\"price\"])/ (df[\"bathrooms\"] +1.0)\n",
    "    df['price_bedrooms'] =  (df[\"price\"])/ (df[\"bedrooms\"] +1.0)\n",
    "    \n",
    "\n",
    "#构造新特征\n",
    "#room_diff：bathroom房间数 - bedroom房间数\n",
    "#room_num：bathroom房间数 + bedroom房间数\n",
    "def create_room_diff_sum(df):\n",
    "    df[\"room_diff\"] = df[\"bathrooms\"] - df[\"bedrooms\"]\n",
    "    df[\"room_num\"] = df[\"bedrooms\"] + df[\"bathrooms\"]\n",
    "\n",
    "    \n",
    "create_price_room(train)\n",
    "create_price_room(test)\n",
    "create_room_diff_sum(train)\n",
    "create_room_diff_sum(test)\n",
    "features_to_use = [\"bathrooms\", \"bedrooms\", \"price_bathrooms\", \"price_bedrooms\", \"room_diff\", \"room_num\", \"price\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建日期created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def procdess_created_date(df):\n",
    "    df['Date'] = pd.to_datetime(df['created'])\n",
    "    df['Year'] = df['Date'].dt.year\n",
    "    df['Month'] = df['Date'].dt.month\n",
    "    df['Day'] = df['Date'].dt.day\n",
    "    df['Wday'] = df['Date'].dt.dayofweek\n",
    "    df['Yday'] = df['Date'].dt.dayofyear\n",
    "    df['hour'] = df['Date'].dt.hour\n",
    "    df.drop(['Date', 'created'], axis=1,inplace = True)\n",
    "\n",
    "procdess_created_date(train)\n",
    "procdess_created_date(test)\n",
    "features_to_use.extend([\"Year\", \"Month\", \"Day\", \"Wday\", \"Yday\", \"hour\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bathrooms',\n",
       " 'bedrooms',\n",
       " 'price_bathrooms',\n",
       " 'price_bedrooms',\n",
       " 'room_diff',\n",
       " 'room_num',\n",
       " 'price',\n",
       " 'Year',\n",
       " 'Month',\n",
       " 'Day',\n",
       " 'Wday',\n",
       " 'Yday',\n",
       " 'hour']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 描述词汇数量\n",
    "train['num_description_words'] = train['description'].apply(lambda x: len(x.split(\" \")))\n",
    "test['num_description_words'] = test['description'].apply(lambda x: len(x.split(\" \")))\n",
    "\n",
    "features_to_use.extend([\"num_description_words\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## display_address、manager_id、building_id、street_address"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [\"display_address\", \"manager_id\",'building_id',\"street_address\"]\n",
    "for f in categorical:\n",
    "    if train[f].dtype == 'object':\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(train[f].values) + list(test[f].values))\n",
    "        train[f] = lbl.transform(list(train[f].values))\n",
    "        test[f] = lbl.transform(list(test[f].values))\n",
    "        features_to_use.append(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## photos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 照片数量(num_photos)\n",
    "train['num_photos'] = train['photos'].apply(len)\n",
    "test['num_photos'] = test['photos'].apply(len)\n",
    "features_to_use.extend([\"num_photos\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## latitude, longtitude\n",
    "聚类降维编码(#用训练数据训练，对训练数据和测试数据都做变换)\n",
    "到中心的距离（论坛上讨论到曼哈顿中心的距离更好）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def procdess_location_train(df):   \n",
    "    train_location = df.loc[:,[ 'latitude', 'longitude']]\n",
    "     # Clustering\n",
    "    kmeans_cluster = KMeans(n_clusters=20)\n",
    "    res = kmeans_cluster.fit(train_location)\n",
    "    res = kmeans_cluster.predict(train_location)\n",
    "    df['cenroid'] = res\n",
    "    # L1 distance\n",
    "    center = [ train_location['latitude'].mean(), train_location['longitude'].mean()]\n",
    "    df['distance'] = abs(df['latitude'] - center[0]) + abs(df['longitude'] - center[1])\n",
    "    #原始特征也可以考虑保留，此处简单丢弃\n",
    "#     df.drop(['latitude', 'longitude'], axis=1, inplace=True)\n",
    "    return kmeans_cluster,center\n",
    "\n",
    "\n",
    "def procdess_location_test(df, kmeans_cluster, center):   \n",
    "    test_location = df.loc[:,[ 'latitude', 'longitude']]\n",
    "     # Clustering\n",
    "    res = kmeans_cluster.predict(test_location)\n",
    "    df['cenroid'] = res\n",
    "    # L1 distance\n",
    "    df['distance'] = abs(df['latitude'] - center[0]) + abs(df['longitude'] - center[1])\n",
    "#     df.drop(['latitude', 'longitude'], axis=1, inplace=True)\n",
    "    \n",
    "\n",
    "### 计算train到曼哈顿中心的距离\n",
    "kmeans_cluster,center = procdess_location_train(train)\n",
    "### 计算test到曼哈顿中心的距离\n",
    "procdess_location_test(test, kmeans_cluster, center)\n",
    "\n",
    "features_to_use.extend([\"distance\", \"latitude\", \"longitude\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 标签interest_level\n",
    "\n",
    "### 将类别型的标签interest_level编码为数字\n",
    "从前面的分析和常识来看，listing_id对确定interest_level没有用，去掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_map = {'low': 2, 'medium': 1, 'high': 0}\n",
    "train_y = train['interest_level'].apply(lambda x: y_map[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 特征数量(num_features)\n",
    "train['num_features'] = train['features'].apply(len)\n",
    "test['num_features'] = test['features'].apply(len)\n",
    "# features_to_use.extend([\"num_features\", \"features\"])\n",
    "features_to_use.extend([\"num_features\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price_bathrooms</th>\n",
       "      <th>price_bedrooms</th>\n",
       "      <th>room_diff</th>\n",
       "      <th>room_num</th>\n",
       "      <th>price</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>...</th>\n",
       "      <th>num_description_words</th>\n",
       "      <th>display_address</th>\n",
       "      <th>manager_id</th>\n",
       "      <th>building_id</th>\n",
       "      <th>street_address</th>\n",
       "      <th>num_photos</th>\n",
       "      <th>distance</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>num_features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>1475.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2950</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>78</td>\n",
       "      <td>13274</td>\n",
       "      <td>3076</td>\n",
       "      <td>5535</td>\n",
       "      <td>24898</td>\n",
       "      <td>8</td>\n",
       "      <td>0.053829</td>\n",
       "      <td>40.7185</td>\n",
       "      <td>-73.9865</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2850</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>13391</td>\n",
       "      <td>3593</td>\n",
       "      <td>0</td>\n",
       "      <td>5492</td>\n",
       "      <td>3</td>\n",
       "      <td>0.058029</td>\n",
       "      <td>40.7278</td>\n",
       "      <td>-74.0000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>1879.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3758</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>333</td>\n",
       "      <td>990</td>\n",
       "      <td>2677</td>\n",
       "      <td>2813</td>\n",
       "      <td>541</td>\n",
       "      <td>6</td>\n",
       "      <td>0.044229</td>\n",
       "      <td>40.7306</td>\n",
       "      <td>-73.9890</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1100.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3300</td>\n",
       "      <td>2016</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>204</td>\n",
       "      <td>481</td>\n",
       "      <td>201</td>\n",
       "      <td>5477</td>\n",
       "      <td>10531</td>\n",
       "      <td>6</td>\n",
       "      <td>0.032029</td>\n",
       "      <td>40.7109</td>\n",
       "      <td>-73.9571</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100000</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>1633.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4900</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>174</td>\n",
       "      <td>12317</td>\n",
       "      <td>3157</td>\n",
       "      <td>4428</td>\n",
       "      <td>10907</td>\n",
       "      <td>7</td>\n",
       "      <td>0.052240</td>\n",
       "      <td>40.7650</td>\n",
       "      <td>-73.9845</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        bathrooms  bedrooms  price_bathrooms  price_bedrooms  room_diff  \\\n",
       "0             1.0         1      1475.000000     1475.000000        0.0   \n",
       "1             1.0         2      1425.000000      950.000000       -1.0   \n",
       "100           1.0         1      1879.000000     1879.000000        0.0   \n",
       "1000          1.0         2      1650.000000     1100.000000       -1.0   \n",
       "100000        2.0         2      1633.333333     1633.333333        0.0   \n",
       "\n",
       "        room_num  price  Year  Month  Day      ...       \\\n",
       "0            2.0   2950  2016      6   11      ...        \n",
       "1            3.0   2850  2016      6   24      ...        \n",
       "100          2.0   3758  2016      6    3      ...        \n",
       "1000         3.0   3300  2016      6   11      ...        \n",
       "100000       4.0   4900  2016      4   12      ...        \n",
       "\n",
       "        num_description_words  display_address  manager_id  building_id  \\\n",
       "0                          78            13274        3076         5535   \n",
       "1                          35            13391        3593            0   \n",
       "100                       333              990        2677         2813   \n",
       "1000                      204              481         201         5477   \n",
       "100000                    174            12317        3157         4428   \n",
       "\n",
       "        street_address  num_photos  distance  latitude  longitude  \\\n",
       "0                24898           8  0.053829   40.7185   -73.9865   \n",
       "1                 5492           3  0.058029   40.7278   -74.0000   \n",
       "100                541           6  0.044229   40.7306   -73.9890   \n",
       "1000             10531           6  0.032029   40.7109   -73.9571   \n",
       "100000           10907           7  0.052240   40.7650   -73.9845   \n",
       "\n",
       "        num_features  \n",
       "0                  6  \n",
       "1                  3  \n",
       "100                3  \n",
       "1000              10  \n",
       "100000            14  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_to_use_train = train[features_to_use]\n",
    "features_to_use_test = test[features_to_use]\n",
    "features_to_use_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "features特征中单词的词频，相当于以数据集features中出现的词语为字典的one-hot编码（虽然是词频，但在这个任务中每个单词通常只出现一次）\n",
    "\n",
    "这里由于机器跑的太慢，暂时不考虑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "# def procdess_features_train_test(df_train, df_test):\n",
    "#     n_train_samples = len(df_train.index)\n",
    "    \n",
    "#     df_train_test = pd.concat((df_train, df_test), axis=0)\n",
    "#     df_train_test['features2'] = df_train_test['features']\n",
    "#     df_train_test['features2'] = df_train_test['features2'].apply(lambda x: ' '.join(x))\n",
    "\n",
    "#     c_vect = CountVectorizer(stop_words='english', max_features=200, ngram_range=(1, 1), decode_error='ignore')\n",
    "#     c_vect_sparse = c_vect.fit_transform(df_train_test['features2'])\n",
    "#     c_vect_sparse_cols = c_vect.get_feature_names()\n",
    "\n",
    "#     df_train.drop(['features'], axis=1, inplace=True)\n",
    "#     df_test.drop(['features'], axis=1, inplace=True)\n",
    "    \n",
    "#     #hstack作为特征处理的最后一部，先将其他所有特征都转换成数值型特征才能处理,稀疏表示\n",
    "#     df_train_sparse = sparse.hstack([df_train, c_vect_sparse[:n_train_samples,:]]).tocsr()\n",
    "#     df_test_sparse = sparse.hstack([df_test, c_vect_sparse[n_train_samples:,:]]).tocsr()\n",
    "    \n",
    "#     #常规datafrmae\n",
    "#     tmp = pd.DataFrame(c_vect_sparse.toarray()[:n_train_samples,:],columns = c_vect_sparse_cols, index=df_train.index)\n",
    "#     df_train = pd.concat([df_train, tmp], axis=1)\n",
    "    \n",
    "#     tmp = pd.DataFrame(c_vect_sparse.toarray()[n_train_samples:,:],columns = c_vect_sparse_cols, index=df_test.index)\n",
    "#     df_test = pd.concat([df_test, tmp], axis=1)\n",
    "    \n",
    "#     #df_test = pd.concat([df_test, tmp[n_train_samples:,:]], axis=1)\n",
    "  \n",
    "#     return df_train_sparse,df_test_sparse,df_train, df_test\n",
    "\n",
    "# X_train_sparse,X_test_sparse, train2, test2 = procdess_features_train_test(features_to_use_train,features_to_use_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train2 = pd.concat([train2, train_y], axis=1)\n",
    "# train2.to_csv('/Users/admin/Desktop/day02/week2data_RentListingInquries/RentListingInquries_FE_train2.csv', index=False)\n",
    "# test2.to_csv('/Users/admin/Desktop/day02/week2data_RentListingInquries/RentListingInquries_FE_test2.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 xgboost\n",
    "独立调用xgboost或在sklearn框架下调用均可。\n",
    "1. 模型训练：超参数调优\n",
    "    - 初步确定弱学习器数目： 20分\n",
    "    - 对树的最大深度（可选）和min_children_weight进行调优（可选）：20分\n",
    "    - 对正则参数进行调优：20分\n",
    "    - 重新调整弱学习器数目：10分\n",
    "    - 行列重采样参数调整：10分\n",
    "2. 调用模型进行测试10分\n",
    "3. 生成测试结果文件10分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 确定弱学习器数目n_estimators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "280\n",
      "logloss: 0.4798558806949912\n"
     ]
    }
   ],
   "source": [
    "X_train = features_to_use_train\n",
    "y_train = train_y\n",
    "\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "xgbClassifier = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        nthread=-1,\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "\n",
    "xgb_param = xgbClassifier.get_xgb_params()\n",
    "xgb_param['num_class'] = 3\n",
    "xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "\n",
    "cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=xgbClassifier.get_params()['n_estimators'], folds=kfold,\n",
    "             metrics='mlogloss', early_stopping_rounds=10)\n",
    "      \n",
    "#最佳参数n_estimators\n",
    "n_estimators = cvresult.shape[0]\n",
    "\n",
    "# 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "xgbClassifier.set_params(n_estimators = n_estimators)\n",
    "xgbClassifier.fit(X_train,y_train,eval_metric='mlogloss')\n",
    "print(n_estimators)\n",
    "\n",
    "train_predprob = xgbClassifier.predict_proba(X_train)\n",
    "logloss = log_loss(y_train, train_predprob)\n",
    "print(\"logloss:\", logloss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### n_estimators最优值为280"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "test_means = cvresult['test-mlogloss-mean']\n",
    "test_stds = cvresult['test-mlogloss-std'] \n",
    "        \n",
    "train_means = cvresult['train-mlogloss-mean']\n",
    "train_stds = cvresult['train-mlogloss-std'] \n",
    "\n",
    "x_axis = range(0, cvresult.shape[0])\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "plt.errorbar(x_axis, test_means, yerr=test_stds ,label='Test')\n",
    "plt.errorbar(x_axis, train_means, yerr=train_stds ,label='Train')\n",
    "plt.xlabel('n_estimators:')\n",
    "plt.ylabel('logloss:')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 对树的最大深度max_depth和min_children_weight进行调优\n",
    "\n",
    "根据Owen Zhang (常使用XGBoost)建议\n",
    "\n",
    "- 树的深度max_depth:从6开始，然后逐步加大\n",
    "- min_child_weight : 1/sqrt(rare_events)，其中rare_events 为稀有事件的数目\n",
    "\n",
    "这里：\n",
    "- 树的深度max_depth这里从5取到9\n",
    "- min_child_weight从3到8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid_scores_: [mean: -0.57146, std: 0.00445, params: {'max_depth': 5, 'min_child_weight': 4}, mean: -0.57197, std: 0.00495, params: {'max_depth': 5, 'min_child_weight': 5}, mean: -0.57130, std: 0.00444, params: {'max_depth': 5, 'min_child_weight': 6}, mean: -0.57070, std: 0.00424, params: {'max_depth': 5, 'min_child_weight': 7}, mean: -0.57112, std: 0.00405, params: {'max_depth': 5, 'min_child_weight': 8}, mean: -0.57020, std: 0.00542, params: {'max_depth': 6, 'min_child_weight': 4}, mean: -0.56943, std: 0.00541, params: {'max_depth': 6, 'min_child_weight': 5}, mean: -0.57003, std: 0.00551, params: {'max_depth': 6, 'min_child_weight': 6}, mean: -0.57031, std: 0.00600, params: {'max_depth': 6, 'min_child_weight': 7}, mean: -0.56944, std: 0.00484, params: {'max_depth': 6, 'min_child_weight': 8}, mean: -0.57149, std: 0.00485, params: {'max_depth': 7, 'min_child_weight': 4}, mean: -0.57129, std: 0.00470, params: {'max_depth': 7, 'min_child_weight': 5}, mean: -0.57206, std: 0.00537, params: {'max_depth': 7, 'min_child_weight': 6}, mean: -0.57163, std: 0.00579, params: {'max_depth': 7, 'min_child_weight': 7}, mean: -0.57134, std: 0.00454, params: {'max_depth': 7, 'min_child_weight': 8}, mean: -0.57876, std: 0.00653, params: {'max_depth': 8, 'min_child_weight': 4}, mean: -0.57680, std: 0.00734, params: {'max_depth': 8, 'min_child_weight': 5}, mean: -0.57509, std: 0.00650, params: {'max_depth': 8, 'min_child_weight': 6}, mean: -0.57449, std: 0.00614, params: {'max_depth': 8, 'min_child_weight': 7}, mean: -0.57403, std: 0.00672, params: {'max_depth': 8, 'min_child_weight': 8}, mean: -0.58411, std: 0.00562, params: {'max_depth': 9, 'min_child_weight': 4}, mean: -0.58276, std: 0.00578, params: {'max_depth': 9, 'min_child_weight': 5}, mean: -0.57944, std: 0.00755, params: {'max_depth': 9, 'min_child_weight': 6}, mean: -0.58027, std: 0.00614, params: {'max_depth': 9, 'min_child_weight': 7}, mean: -0.57701, std: 0.00595, params: {'max_depth': 9, 'min_child_weight': 8}]\n",
      "best_params_: {'max_depth': 6, 'min_child_weight': 5}\n",
      "best_score_: -0.5694338823117654\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "max_depth = [5, 6, 7, 8, 9]\n",
    "min_child_weight = [4, 5, 6, 7, 8]\n",
    "param_grid = dict(max_depth=max_depth, min_child_weight=min_child_weight)\n",
    "#第一轮参数调整得到的n_estimators最优值238\n",
    "xgbClassifier = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=280,\n",
    "        max_depth=5,\n",
    "        min_child_weight=1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "gridSearch = GridSearchCV(xgbClassifier, param_grid = param_grid, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gridSearch.fit(X_train, y_train)\n",
    "print(\"grid_scores_:\", gridSearch.grid_scores_)\n",
    "print(\"best_params_:\", gridSearch.best_params_)\n",
    "print(\"best_score_:\", gridSearch.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### max_depth最优值为6，min_child_weight最优值为5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gridSearch.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'logloss')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_means = gridSearch.cv_results_['mean_test_score']\n",
    "test_stds = gridSearch.cv_results_['std_test_score']\n",
    "train_means = gridSearch.cv_results_['mean_train_score']\n",
    "train_stds = gridSearch.cv_results_['std_train_score']\n",
    "\n",
    "train_scores = np.array(train_means).reshape(len(min_child_weight), len(max_depth))\n",
    "test_scores = np.array(test_means).reshape(len(min_child_weight), len(max_depth))\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "for i, value in enumerate(min_child_weight):\n",
    "    plt.plot(max_depth, test_scores[i], label= 'test_min_child_weight:' + str(value))\n",
    "plt.legend()\n",
    "plt.xlabel('max_depth')                                                                                                      \n",
    "plt.ylabel('logloss')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 正则参数reg_alpha和reg_lambda\n",
    "\n",
    "- reg_alpha = [ 0.1, 1] # L1, default = 0\n",
    "- reg_lambda = [0.5, 1, 2] # L2, default = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reg_alpha = [0.1, 1, 1.5, 2]\n",
    "reg_lambda = [0.1, 0.5, 1, 2]\n",
    "param_grid = dict(reg_alpha=reg_alpha, reg_lambda=reg_lambda)\n",
    "\n",
    "\n",
    "#第一轮参数调整得到的n_estimators最优值280\n",
    "#第二轮参数调整得到max_depth最优值为6，min_child_weight最优值为7\n",
    "xgbClassifier = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=280,\n",
    "        max_depth=6,\n",
    "        min_child_weight=5,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "gridSearch = GridSearchCV(xgbClassifier, param_grid = param_grid, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gridSearch.fit(X_train, y_train)\n",
    "\n",
    "print(\"grid_scores_:\", gridSearch.grid_scores_)\n",
    "print(\"best_params_:\", gridSearch.best_params_)\n",
    "print(\"best_score_:\", gridSearch.best_score_)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则参数reg_alpha最优值为1，reg_lambda最优值为0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gridSearch.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'logloss')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_means = gridSearch.cv_results_['mean_test_score']\n",
    "test_stds = gridSearch.cv_results_['std_test_score']\n",
    "train_means = gridSearch.cv_results_['mean_train_score']\n",
    "train_stds = gridSearch.cv_results_['std_train_score']\n",
    "\n",
    "train_scores = np.array(train_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "test_scores = np.array(test_means).reshape(len(reg_alpha), len(reg_lambda))\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "for i, value in enumerate(reg_alpha):\n",
    "    plt.plot(reg_lambda, test_scores[i], label= 'test_reg_alpha:' +  str(value))\n",
    "plt.legend()\n",
    "plt.xlabel('reg_lambda')                                                                                                      \n",
    "plt.ylabel('logloss')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4行列采样参数: subsample和colsample_bytree\n",
    "\n",
    "\n",
    "这两个参数可分别对样本和特征进行采样，从而增加模型的鲁棒性\n",
    "- 现在0.3至1之间调整，粗调时步长0.1\n",
    "- 微调时步长为0.05(略)\n",
    "\n",
    "注:colsample_bylevel通常0.7-0.8可以得到较好的结果。如计算资源允许， 也可以进一步调整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid_scores_: [mean: -0.56921, std: 0.00424, params: {'colsample_bytree': 0.6, 'subsample': 0.3}, mean: -0.56627, std: 0.00540, params: {'colsample_bytree': 0.6, 'subsample': 0.4}, mean: -0.56421, std: 0.00544, params: {'colsample_bytree': 0.6, 'subsample': 0.5}, mean: -0.56325, std: 0.00508, params: {'colsample_bytree': 0.6, 'subsample': 0.6}, mean: -0.56205, std: 0.00487, params: {'colsample_bytree': 0.6, 'subsample': 0.7}, mean: -0.56103, std: 0.00513, params: {'colsample_bytree': 0.6, 'subsample': 0.8}, mean: -0.56159, std: 0.00494, params: {'colsample_bytree': 0.6, 'subsample': 0.9}, mean: -0.56881, std: 0.00653, params: {'colsample_bytree': 0.7, 'subsample': 0.3}, mean: -0.56589, std: 0.00429, params: {'colsample_bytree': 0.7, 'subsample': 0.4}, mean: -0.56427, std: 0.00473, params: {'colsample_bytree': 0.7, 'subsample': 0.5}, mean: -0.56357, std: 0.00413, params: {'colsample_bytree': 0.7, 'subsample': 0.6}, mean: -0.56155, std: 0.00454, params: {'colsample_bytree': 0.7, 'subsample': 0.7}, mean: -0.56087, std: 0.00415, params: {'colsample_bytree': 0.7, 'subsample': 0.8}, mean: -0.56084, std: 0.00478, params: {'colsample_bytree': 0.7, 'subsample': 0.9}, mean: -0.56991, std: 0.00487, params: {'colsample_bytree': 0.8, 'subsample': 0.3}, mean: -0.56764, std: 0.00451, params: {'colsample_bytree': 0.8, 'subsample': 0.4}, mean: -0.56555, std: 0.00562, params: {'colsample_bytree': 0.8, 'subsample': 0.5}, mean: -0.56277, std: 0.00494, params: {'colsample_bytree': 0.8, 'subsample': 0.6}, mean: -0.56262, std: 0.00512, params: {'colsample_bytree': 0.8, 'subsample': 0.7}, mean: -0.56087, std: 0.00467, params: {'colsample_bytree': 0.8, 'subsample': 0.8}, mean: -0.56100, std: 0.00458, params: {'colsample_bytree': 0.8, 'subsample': 0.9}, mean: -0.56934, std: 0.00440, params: {'colsample_bytree': 0.9, 'subsample': 0.3}, mean: -0.56681, std: 0.00608, params: {'colsample_bytree': 0.9, 'subsample': 0.4}, mean: -0.56566, std: 0.00559, params: {'colsample_bytree': 0.9, 'subsample': 0.5}, mean: -0.56344, std: 0.00538, params: {'colsample_bytree': 0.9, 'subsample': 0.6}, mean: -0.56256, std: 0.00534, params: {'colsample_bytree': 0.9, 'subsample': 0.7}, mean: -0.56135, std: 0.00491, params: {'colsample_bytree': 0.9, 'subsample': 0.8}, mean: -0.56182, std: 0.00503, params: {'colsample_bytree': 0.9, 'subsample': 0.9}]\n",
      "best_params_: {'colsample_bytree': 0.7, 'subsample': 0.9}\n",
      "best_score_: -0.5608351461464203\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "subsample = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "colsample_bytree = [0.6, 0.7, 0.8, 0.9]\n",
    "param_grid = dict(subsample=subsample, colsample_bytree=colsample_bytree)\n",
    "\n",
    "\n",
    "#第一轮参数调整得到的n_estimators最优值238\n",
    "#第二轮参数调整得到max_depth最优值为6，min_child_weight最优值为7\n",
    "#第三轮参数调整得到reg_alpha最优值为1.5，reg_lambda最优值为0.1\n",
    "xgbClassifier = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=280,\n",
    "        max_depth=6,\n",
    "        min_child_weight=5,\n",
    "        reg_alpha = 1,\n",
    "        reg_lambda = 0.1,\n",
    "        gamma=0,\n",
    "        subsample=0.3,\n",
    "        colsample_bytree=0.8,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "gridSearch = GridSearchCV(xgbClassifier, param_grid = param_grid, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gridSearch.fit(X_train, y_train)\n",
    "\n",
    "print(\"grid_scores_:\", gridSearch.grid_scores_)\n",
    "print(\"best_params_:\", gridSearch.best_params_)\n",
    "print(\"best_score_:\", gridSearch.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### colsample_bytree最优值为0.7，subsample最优值为0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gridSearch.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'logloss')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_means = gridSearch.cv_results_['mean_test_score']\n",
    "test_stds = gridSearch.cv_results_['std_test_score']\n",
    "train_means = gridSearch.cv_results_['mean_train_score']\n",
    "train_stds = gridSearch.cv_results_['std_train_score']\n",
    "\n",
    "train_scores = np.array(train_means).reshape(len(colsample_bytree), len(subsample))\n",
    "test_scores = np.array(test_means).reshape(len(colsample_bytree), len(subsample))\n",
    "\n",
    "fig = plt.figure(figsize=(16, 10))\n",
    "for i, value in enumerate(colsample_bytree):\n",
    "    plt.plot(subsample, -test_scores[i], label= 'test_colsample_bytree:' +  str(value))\n",
    "plt.legend()\n",
    "plt.xlabel('subsample')                                                                                                      \n",
    "plt.ylabel('logloss')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 重新调整弱学习器数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "307\n",
      "logloss: 0.42235095567571584\n"
     ]
    }
   ],
   "source": [
    "xgbClassifier = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=1000,\n",
    "        max_depth=6,\n",
    "        min_child_weight=5,\n",
    "        reg_alpha = 1,\n",
    "        reg_lambda = 0.1,\n",
    "        gamma=0,\n",
    "        subsample=0.9,\n",
    "        colsample_bytree=0.7,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "\n",
    "xgb_param = xgbClassifier.get_xgb_params()\n",
    "xgb_param['num_class'] = 3\n",
    "xgtrain = xgb.DMatrix(X_train, label = y_train)\n",
    "\n",
    "cvresult = xgb.cv(xgb_param, xgtrain, num_boost_round=xgbClassifier.get_params()['n_estimators'], folds=kfold,\n",
    "             metrics='mlogloss', early_stopping_rounds=10)\n",
    "      \n",
    "#最佳参数n_estimators\n",
    "n_estimators = cvresult.shape[0]\n",
    "\n",
    "# 采用交叉验证得到的最佳参数n_estimators，训练模型\n",
    "xgbClassifier.set_params(n_estimators = n_estimators)\n",
    "xgbClassifier.fit(X_train,y_train,eval_metric='mlogloss')\n",
    "print(n_estimators)\n",
    "\n",
    "train_predprob = xgbClassifier.predict_proba(X_train)\n",
    "logloss = log_loss(y_train, train_predprob)\n",
    "print(\"logloss:\", logloss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最佳n_estimators=307"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00606187, 0.00861149, 0.06230975, 0.07237818, 0.01147332,\n",
       "       0.00973021, 0.05848531, 0.        , 0.00608788, 0.03845253,\n",
       "       0.02057913, 0.04896324, 0.03629316, 0.06324635, 0.06608216,\n",
       "       0.09196868, 0.05211125, 0.06111299, 0.03332726, 0.07029685,\n",
       "       0.0711554 , 0.06993262, 0.04134037], dtype=float32)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbClassifier.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6 行列采样参数: subsample和colsample_bytree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grid_scores_: [mean: -0.56900, std: 0.00452, params: {'colsample_bytree': 0.6, 'subsample': 0.3}, mean: -0.56579, std: 0.00529, params: {'colsample_bytree': 0.6, 'subsample': 0.4}, mean: -0.56380, std: 0.00549, params: {'colsample_bytree': 0.6, 'subsample': 0.5}, mean: -0.56273, std: 0.00533, params: {'colsample_bytree': 0.6, 'subsample': 0.6}, mean: -0.56155, std: 0.00512, params: {'colsample_bytree': 0.6, 'subsample': 0.7}, mean: -0.56043, std: 0.00536, params: {'colsample_bytree': 0.6, 'subsample': 0.8}, mean: -0.56076, std: 0.00523, params: {'colsample_bytree': 0.6, 'subsample': 0.9}, mean: -0.56868, std: 0.00650, params: {'colsample_bytree': 0.7, 'subsample': 0.3}, mean: -0.56554, std: 0.00434, params: {'colsample_bytree': 0.7, 'subsample': 0.4}, mean: -0.56384, std: 0.00497, params: {'colsample_bytree': 0.7, 'subsample': 0.5}, mean: -0.56342, std: 0.00425, params: {'colsample_bytree': 0.7, 'subsample': 0.6}, mean: -0.56099, std: 0.00463, params: {'colsample_bytree': 0.7, 'subsample': 0.7}, mean: -0.56040, std: 0.00430, params: {'colsample_bytree': 0.7, 'subsample': 0.8}, mean: -0.56009, std: 0.00480, params: {'colsample_bytree': 0.7, 'subsample': 0.9}, mean: -0.57016, std: 0.00518, params: {'colsample_bytree': 0.8, 'subsample': 0.3}, mean: -0.56798, std: 0.00477, params: {'colsample_bytree': 0.8, 'subsample': 0.4}, mean: -0.56544, std: 0.00594, params: {'colsample_bytree': 0.8, 'subsample': 0.5}, mean: -0.56276, std: 0.00489, params: {'colsample_bytree': 0.8, 'subsample': 0.6}, mean: -0.56247, std: 0.00545, params: {'colsample_bytree': 0.8, 'subsample': 0.7}, mean: -0.56028, std: 0.00487, params: {'colsample_bytree': 0.8, 'subsample': 0.8}, mean: -0.56033, std: 0.00473, params: {'colsample_bytree': 0.8, 'subsample': 0.9}, mean: -0.56921, std: 0.00448, params: {'colsample_bytree': 0.9, 'subsample': 0.3}, mean: -0.56690, std: 0.00624, params: {'colsample_bytree': 0.9, 'subsample': 0.4}, mean: -0.56601, std: 0.00553, params: {'colsample_bytree': 0.9, 'subsample': 0.5}, mean: -0.56404, std: 0.00534, params: {'colsample_bytree': 0.9, 'subsample': 0.6}, mean: -0.56248, std: 0.00506, params: {'colsample_bytree': 0.9, 'subsample': 0.7}, mean: -0.56106, std: 0.00515, params: {'colsample_bytree': 0.9, 'subsample': 0.8}, mean: -0.56124, std: 0.00539, params: {'colsample_bytree': 0.9, 'subsample': 0.9}]\n",
      "best_params_: {'colsample_bytree': 0.7, 'subsample': 0.9}\n",
      "best_score_: -0.5600890628604468\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/model_selection/_search.py:761: DeprecationWarning: The grid_scores_ attribute was deprecated in version 0.18 in favor of the more elaborate cv_results_ attribute. The grid_scores_ attribute will not be available from 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "subsample = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "colsample_bytree = [0.6, 0.7, 0.8, 0.9]\n",
    "param_grid = dict(subsample=subsample, colsample_bytree=colsample_bytree)\n",
    "\n",
    "\n",
    "#第一轮参数调整得到的n_estimators最优值238\n",
    "#第二轮参数调整得到max_depth最优值为6，min_child_weight最优值为7\n",
    "#第三轮参数调整得到reg_alpha最优值为1.5，reg_lambda最优值为0.1\n",
    "xgbClassifier2 = XGBClassifier(\n",
    "        learning_rate =0.1,\n",
    "        n_estimators=307,\n",
    "        max_depth=6,\n",
    "        min_child_weight=5,\n",
    "        reg_alpha = 1,\n",
    "        reg_lambda = 0.1,\n",
    "        gamma=0,\n",
    "        subsample=0.9,\n",
    "        colsample_bytree=0.7,\n",
    "        colsample_bylevel=0.7,\n",
    "        objective= 'multi:softprob',\n",
    "        seed=3)\n",
    "\n",
    "kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=3)\n",
    "gridSearch = GridSearchCV(xgbClassifier2, param_grid = param_grid, scoring='neg_log_loss',n_jobs=-1, cv=kfold)\n",
    "gridSearch.fit(X_train, y_train)\n",
    "\n",
    "print(\"grid_scores_:\", gridSearch.grid_scores_)\n",
    "print(\"best_params_:\", gridSearch.best_params_)\n",
    "print(\"best_score_:\", gridSearch.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "colsample_bytree为0.7，subsample为0.9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 调用模型进行测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2\n",
       "0  0.104630  0.549058  0.346312\n",
       "1  0.113954  0.126915  0.759131\n",
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     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "preds = xgbClassifier.predict_proba(features_to_use_test)\n",
    "result = pd.DataFrame(preds)\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 生成测试结果文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.columns = [\"high\", \"medium\", \"low\"]\n",
    "result['id'] = range(0, features_to_use_test.shape[0])\n",
    "result.to_csv(\"result.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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